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Social Sustainable Supplier Evaluation and Selection: A Group Decision Support
Approach
Chunguang Bai
School of Management and Economics
University of Electronic Science and Technology of China
No.2006, Xiyuan Ave, West Hi-Tech Zone, 611731, Chengdu, P.R.China
Tel: 86-13664228458
E-mail: [email protected]
Simonov Kusi-Sarpong (Corresponding Author)
Eco-Engineering and Management Consult Limited
Ti’s – Adentan, Accra - Ghana
Portsmouth Business School
University of Portsmouth
Portland Building, Portland Street
Portsmouth PO1 3AH, United Kingdom
E-mail: [email protected]
Hadi Badri Ahmadi
School of Management Science and Engineering
Dalian University of Technology
No .2 Linggong Road, Ganjingzi District
Dalian Liaoning Province (116023) P.R of China
Tel: (86188) 0428 3152
E-mail: [email protected]
Joseph Sarkis
Foisie School of Business
Worcester Polytechnic Institute
100 Institute Road
Worcester, MA 01609-2280, USA
Tel: (508) 831-4831
E-mail: [email protected]
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Social Sustainable Supplier Evaluation and Selection: A Group Decision Support
Approach
Abstract - Organizational and managerial decisions are influenced by corporate sustainability
pressures. Organizations need to consider economic, environmental and social sustainability
dimensions in their decisions to become sustainable. Supply chain decisions play a distinct
and critical role in organizational good and service outputs sustainability. Sustainable supplier
selection influences the supply chain sustainability allowing many organizations to build
competitive advantage. Within this context, the social sustainability dimension has received
relatively minor investigation; with emphasis typically on economic and environmental
sustainability. Neglecting social sustainability can have serious repercussions for
organizational supply chains. This study proposes a social sustainability attribute decision
framework to evaluate and select socially sustainable suppliers. A grey-based multi-criteria
decision-support tool composed of the ‘best-worst method’ (BWM) and TODIM (TOmada de
Decisão Interativa e Multicritério – in Portuguese “Interactive and Multicriteria Decision
Making”) is introduced. A grey-BWM approach is used to determine social sustainability
attribute weights, and a grey-TODIM method is utilized to rank suppliers. This process is
completed in a group decision setting. A case study of an Iranian manufacturing company is
used to exemplify the applicability and suitability of the proposed social sustainability
decision framework. Managerial implications, limitations, and future research directions are
introduced after application of the model.
Keywords: sustainability; social sustainability; sustainable supply chains; best worst method; BWM;
TODIM
1. Introduction
Regulatory demands and stakeholder awareness, have increased pressures and
caused organizations to explicitly consider sustainability in their decisions (Luthra et
al., 2017; Mathivathanan et al., 2017; Rezaei et al., 2016; Shi et al., 2017; Zhang et
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al., 2016). Firms are not only reacting to pressures, but have also started to recognize
the benefits and importance of sustainability initiatives to build competitive advantage
(Wolf, 2014; Bai et al., 2017; Kusi-Sarpong et al., 2016a, b; Agyemang et al., 2018).
Various initiatives are adopted for these organizations to remain competitive
including supply chain decisions such as low-cost sourcing (D'Eusanio et al., 2018).
But organizations have been faced with social issues resulting from their supply chain
operations; typically from their suppliers (upstream) (Morais and Silvestre, 2018). For
example, poor testing of materials by a supplier may result in dangerous and harmful
products flowing to consumers with higher costs, poorer reputation, and lowered
revenue as outcomes (Klassen and Vereecke, 2012).
These suppliers’ serious social consequences range from strike actions due to poor
work, health and safety conditions, to employee rights related to poor employment
practices such as pay inequities and slave labor conditions (Badri Ahmadi et al.,
2017a, b). These supplier actions result in production losses and the inability to meet
buying firms’ deadlines. Large multinational companies such as Nike, Apple, and
Wal-Mart have faces all these pressures and are addressing these issues by focusing
on the supply chain (Klassen and Vereecke, 2012).
Since suppliers provide raw materials, services and finished products as inputs to
organizational supply chains, their activities are critical to helping organizations
achieve a sustainable and collaborative competitive edge. Supplier performance
directly affects the performance of buying organizations. To more fully address
negative societal images a buying organization requires careful supplier evaluation
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and selection. The resource based view (RBV) (Barney et al., 2001) is a valuable
theoretical lens to argue for the need for social sustainability in organizational supply
chains (Gold et al., 2010). RBV stipulates that organizations can build competitive
capabilities, and advantages, by selecting socially sustainable suppliers. Socially
sustainable suppliers offer valuable intangible resources that help improve
organizational image, improve business continuity, and reduces cost. Supplier
selection is an important and strategic decision in supply chains that can improve
overall social sustainability of products and services (Sucky, 2007; Badri Ahmadi et
al., 2017a). Selecting and working with socially conscious suppliers is important for
maintaining buying organization reputation. Organizations need to consider external
sustainable capabilities, practices and strategies, especially with respect to their
suppliers (Kusi-Sarpong et al., 2015, 2018a). The sustainable supplier selection
process can help determine and balance economic-based supplier capabilities while
considering social and environmental capabilities and attributes (Genovese et al.,
2010). Thus, using RBV, appropriate sustainable supplier selection can help
organizations build or maintain their own social and other sustainability capabilities
contributing to their strategic competitive advantages.
Many studies have investigated supplier selection and evaluation; many focusing
on traditional business and economic criteria (e.g. Pitchipoo et al., 2013; Sevkli, 2010;
Labib, 2011; Dotoli & Falagario, 2012; Rao et al., 2017a). A growing number have
incorporated environmental sustainability (green) criteria (e.g. Kuo & Lin, 2012;
Genovese et al., 2013; Lu et al., 2007; Büyüközkan, 2012; Rao et al., 2015, 2017b,
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2017c) . Other studies have considered supplier selection with broader sustainability
criteria (e.g Azadnia et al., 2015; Gualandris and Kalchschmidt, 2016; Dai and
Blackhurst, 2012; Moheb-Alizadeh & Handfield, 2017; Fabbe-Costes et al., 2014;
Khan et al., 2018; Amindoust, 2018). Few studies have incorporated social
sustainability (Badri Ahmadi et al. 2017b; Mani et al., 2016a, b; Sarkis and Zhu,
2018), none of these studies have focused on selecting and evaluating suppliers solely
on their social sustainability performance.
Supplier selection is a multi-criteria decision-making (MCDM) situation. Many
MCDM models have been proposed and used to support supplier selection including
AHP-QFD (Dai and Blackhurst, 2012), FAHP-GRA (Pitchipoo et al., 2013), Fuzzy
ELECTRE (Sevkli, 2010), ANP-DEA (Kuo & Lin, 2012), DEA-TOPSIS (Dotoli &
Falagario, 2012). Typically these techniques heavily rely on interactive decision
maker involvement, with substantial input from decision makers. This reliance may
cause greater decision maker fatigue, rendering them less practical.
In this paper, an integrated TODIM1, BWM (best-worst method) and grey
number MCDM approach is introduced for socially sustainable supplier evaluation
and selection. TODIM provides value by solving an MCDM problem that
incorporates decision maker behavior. In sustainability-based decision analysis
decision makers are often encumbered with subjective and ambiguous linguistic
1 TOmada de Decisão Interativa e Multicritério – in Portuguese “Interactive and Multicriteria Decision
Making”
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information. Grey-TODIM provides an opportunity to accommodate decision maker
psychological behavior under risk while simultaneously capturing sustainability
decision environment uncertainty; something that other MCDM approaches do not
complete simultaneously.
TODIM requires additional input information, the relative weights of
attributes. This requirement limits its application. BWM is can effectively address this
TODIM requirement. BWM can generate relative attribute weights. Hence, we extend
grey-BWM to determine the relative attribute weights by modifying the objective
function and integrating grey numbers. This multistep method can more effectively
support socially sustainable supplier selection problems considering decision maker
behavior, through prospect theory, in uncertain environments. BWM helps make
TODIM more complete, and more effective, to apply. Integrating BWM and TODIM
methodologies helps to lessen decision maker input and interaction. This study seeks
to address these gaps.
This study adopts and integrates a previously proposed social sustainability
attribute framework into the supplier selection decision problem (see Badri Ahmadi et
al. (2017b)), with the joint grey BWM and TODIM approach. Even though
integration of sustainability triple-bottom-line dimensions (environmental, social and
economic) into the supplier selection decision offers a truly sustainable supplier, this
study focuses only on the use of social dimensions. This focus offers deeper insights
on social sustainability supplier selection and serves as input for comprehensive
sustainable supplier selection decisions. The specific objectives of this paper include:
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1. Introducing a multiple attribute group decision approach integrating grey set
theory with BWM and TODIM for the supplier selection decision;
2. Investigating a multiple attribute socially sustainable supplier evaluation and
selection process within a manufacturing sector context;
3. Providing insights in the practical application of this model within an emerging
economy context (Iran).
This study makes the following academic and managerial contributions: (1)
proposes a social sustainability attributes framework for guiding general social
sustainability decision making; (2) evaluates a multi- criteria decision-making
(MCDM) model integrating interval grey number based BWM and TODIM. Part of
this contribution is a newly formulated BWM model; (3) BWM and interval grey
number are jointly used to overcome TODIM limitations using expert uncertainty
judgments and behavior. The integration of these psychological risk beliefs extends
the literature in this area as well.
The rest of this paper is organized as follows. Section 2 presents background on
sustainable supply chain management, sustainability supplier selection, a social
sustainability attributes framework, and sustainable supplier evaluation and selection
models. The research methodology comprising methods and tools is discussed in
Section 3. In Section 4, a practical case application using the proposed tools is
provided for evaluating the decision support approach’s practical validity. As part of
this practical evaluation a sensitivity analysis is presented in Section 5. Additional
discussion and implications including managerial and post-selection benchmarking
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discussion are presented in Section 6. A summary and conclusion of the study with
identified limitations and opportunities for further research are presented in Section 7.
2. Background
This section initially presents an overview of sustainable supply chain
management and then discusses sustainability supplier selection. Thereafter, a social
sustainability attributes framework is introduced. The section concludes with
sustainable supplier evaluation and selection decision models background discussion.
2.1. Overview of Sustainable supply chain management
Sustainable supply chain management (SSCM) is the process of managing
information and material across the entire supply chain taking into consideration
environmental, social and economic attributes simultaneously (Govindan et al., 2013;
Lin and Tseng, 2016; Reefke and Sundaram, 2018). SSCM helps minimize supply
chain operations negative impacts and improves company efficiency from
environmental, economic and social perspectives (Tseng et al., 2008; Wong et al.,
2014; Chacón Vargas et al., 2018). Managing these sustainability initiatives requires
organizations to balance responsibilities for environmental, social and economic
issues (Bai and Sarkis, 2010a; Sarkis and Zhu, 2018). SSCM studies have devised and
addressed various industrial typologies and contexts (Christmann and Taylor,
2001;Tseng et al., 2015; Azadnia et al., 2015; Govindan et al., 2016; Gualandris et al.,
2016; Ghadimi et al., 2017; and Badri Ahmadi et al., 2017a, b).
Sustainability and sustainable development can enhance organizational supply
chain operations performance contributing to general organizational competitiveness
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(Chardine et al., 2014). Sustainability is usually considered as a mix of economic,
environmental and social development (Gauthier, 2005). SSCM initiatives provide a
pathway for organizations in achieving a “win-win-win” sustainable outcome (Saberi
et al., 2018; Danese et al., 2018; Das, 2018). Firms adopting these initiatives become
more focused on promoting sustainable development; preparing themselves for new
global sustainability initiatives such as the United Nations’ Sustainable Development
Goals (SDG) where sustainable production and consumption are important goals
(Griggs et al., 2013).
2.2. Sustainability-based supplier selection
The critical roles played by suppliers in supply chain management and their
impacts on organizational, product, and goods, sustainable performance require that
their evaluation and selection be rigorous and robust (Ageron et al., 2012; Asadabadi,
2016). With the emergence of sustainable supply chain management, studies have
identified the need to incorporate environmental and social attributes into the
traditional economic-based supplier selection decisions (Zhu et al., 2007; Bai and
Sarkis, 2010a; Song et al., 2017). Many studies on sustainable supplier selection
decisions have emerged (e.g. Amindoust et al., 2012; Azadnia et al., 2015; Badri
Ahmadi et al., 2017a; Bai and Sarkis, 2010a; Genovese et al., 2010; Govindan et al.,
2013; Sarkis and Dhavale, 2015).
Sustainable supplier selection decision tools have focused on environmental and
economic dimensions; giving less attention to social dimensions. An increasing rise of
social and societal issues are facing supply chains, especially in emerging economy
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nations. Various issues including labour agitation from abusive practices; poor
working conditions; and occupational, health and safety problems inherent in
organizations, have warranted the need to focus on the social sustainability dimension
when selecting suppliers (Mani et al., 2016a, b).
Using RBV as the theoretical lens, we argue that there exists a relationship
between social sustainability practices and building competitive advantage and
improved economic performance. For example human resource sustainability has
been linked to improved competitive advantages along the supply chain and in supply
chain partners (Pullman et al., 2009; Mani et al., 2018). Part of this competitive
advantage is through lessened costs where some have found that social sustainability
employee practices resulted in reduced costs (Sroufe and Gopalakrishna-Remani,
2018). The argument is that sustainability characteristics of supply chain partners
enhance the intangible resources available to a buying organization helping them
build a competitive advantage. Thus, effective sustainability-based supplier selection
can build necessary competitive resources for buying organizations.
Recent studies (e.g. Badri Ahmadi et al., 2017b; Mani et al., 2016a, b) have
attempted to address the gap of focusing only on social sustainability from emerging
economies. These initial works have not given as much attention to broader supply
chain management social sustainability implementation decisions. Studies have
incorporated and investigated social sustainability when selecting emerging economy
suppliers (e.g. Ehrgott et al., 2011), but these works focused on drivers and benefits to
be realized for organizations from adopting these initiatives. Few studies on social
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sustainability supplier selection from emerging economies exist. This study expands
on previous studies this area by introducing a new typology for investigating social
sustainability through supplier selection in an emerging economy nation.
2.3. A social sustainability attributes framework
Few studies have introduced social sustainability attributes frameworks for
organizational decision support and promoting sustainability. This study uses a social
sustainability attributes decision framework (Badri Ahmadi et al., 2017b) in an
emerging economy manufacturing sector. The framework consists of eight attributes
including: ‘Work health and safety’; ‘Training education and community influence’;
‘Contractual stakeholder influence’; ‘Occupational health and safety management
system’; ‘The interests and rights of employees’; ‘The rights of stakeholders’;
‘Information disclosure’; and ‘Employment practices’. The broader focus of this study
is to evaluate, rank and select sustainable suppliers based on organizational social
sustainability attributes. The supply chain social sustainability attributes are
summarized with brief explanations in Table 1.
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Table 1: The social sustainability attributes of supply chains
Source: Badri Ahmadi et al. (2017b)
Attributes References Short description
Work health and safety
(SSA1)
Badri Ahmadi et al.
(2017a), Azadnia et al.
(2015), Amindoust et
al. (2012), Aydın
Keskin et al. (2010)
This relates to the firms’ focus on both their
operation’s and that of potential supplier’s
operation’s health and safety practices.
Training education and
community influence
(SSA2)
Azadnia et al.(2015),
Badri Ahmadi et al.
(2017a)
This relates to the transfer and impact of
knowledge from employer to its employees
and the community within which they
operate.
Contractual
stakeholders’ influence
(SSA3)
Presley et al. (2007),
Govindan et al. (2013),
Badri Ahmadi et al.
(2017a)
This relates to the level of attention a
potential supplier pays to its stakeholders to
get involved in its operations.
Occupational health and
safety management
system (SSA4)
Bai and Sarkis (2010a),
Azadnia et al. (2015),
Luthra et al. (2017)
This relates to workers’ health and safety,
and welfare at the workplace.
The interests and rights
of employees (SSA5)
Luthra et al. (2017),
Amindoust et al.
(2012),
Kuo et al. (2010)
This has to do with factors that promote
employee concerns and related sustainable
employment issues.
The rights of
stakeholders (SSA6)
Amindoust et al.
(2012), Kuo et al.
(2010), Luthra et al.
(2017)
This relates to the rights of society, which
has a stake in the business.
Information disclosure
(SSA7)
Kuo et al. (2010),
Luthra et al. (2017),
Amindoust et al. (2012)
This has to do with firms providing their
clients and stakeholders with related
information about the materials being used
during the manufacturing process and carbon
emissions.
Employment practices
(SSA8)
Bai and Sarkis (2010a),
Govindan et al. (2013)
This concerns programs and practices related
to employees.
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2.4. Sustainable supplier evaluation and selection decision models
Supplier selection, as a multi-criteria decision problem has received much
attention in the literature; with an increasing number of decision support techniques
applied. A large increase in studies has occurred due to the complexity of sustainable
supplier selection. This complexity includes inclusion of numerous dimensions and
attributes with varying numerical and factor characteristics, such as tangibility and
level of decision making required. The need for MCDM tools in this context is
self-evident.
Sustainability or green supplier evaluation and selection MCDM tools have been
popular (Bai and Sarkis, 2010a, 2010b; Trapp and Sarkis, 2016). Fuzzy MCDM
methods have also been popular. Fuzzy interfaces (Amindoust et al., 2012),
fuzzy-TOPSIS (Govindan et al., 2013), integrated fuzzy logic and influence diagrams
(Ferreira and Borenstein, 2012) have each been used for assessing and ranking
suppliers.
Other, sustainable supplier selection MCDM tools include TOPSIS, VIKOR and
Grey Relational Analysis (GRA) (Rezaei et al., 2016; Banaeian et al., 2016). Hybrid
methods of AHP, ANP, ELECTREE II and VIKOR have also seen significant
investigation (Jeya et al., 2016; Yo and Hou, 2016). A number of literature surveys on
supplier selection MCDM approaches exist (de boer et al., 2001; Ho et al., 2010; Chai
et al., 2014; Govindan et al., 2015; Asadabadi, 2017).
Most of these MCDM decision support tools are based on the assumption that
decision makers are rational (Bai et al., 2016). However, the psychological behavior
14
of the decision maker plays an important role in decision analysis, and should be
considered in the decision-making process. TODIM uses prospect theory for solving
MCDM problems. Prospect theory considers decision maker psychological behaviors
(Zhang and Xu, 2014). Table 2 provides a summary of some recent papers that apply
TODIM and their context.
Table 2: Some recent papers that apply TODIM and the context
Method(s) Context Author(s)
IF-RTODIM Generalizing the Fuzzy-TODIM method to deal
with intuitionistic fuzzy information
Lourenzutti and
Krohling (2013)
Rough set theory-TODIM Supplier selection and evaluation in sustainable
supply chains Li et al.(2018)
Fuzzy-TODIM Evaluating green supply chain practices under
uncertainty Tseng et al.(2014)
TOPSIS-TODIM
Investigating groups decision-making with
different opinions, heterogeneous types of
information and criteria interaction
Lourenzutti et
al.(2017)
TODIM-FSE Introduces a multi-criteria method for solving oil
spill classification problems
Passos et al.
(2014)
TOPSIS-TODIM
Employing Hellinger distance concept to the
MCDM context to assist the models to deal with
probability distributions
Lourenzutti and
Krohling (2014)
TODIM-PROMETHEE Selecting waste-to-energy plant site based on
sustainability perspective Wu et al. (2018)
TODIM Multi-criteria rental evaluation of residential
properties in Brazil
Gomes and
Rangel (2009)
TODIM Proposing a risk decision analysis method in
emergency response context Li and Cao (2018)
TIFNs-TODIM Investigating a renewable energy selection
problem Qin et al. (2017)
Variations in rational and irrational decision-maker preferences and judgments
causes greater uncertainty. Assigning exact values to precisely describe
decision-maker judgments, may become a fool’s errand. Interval grey numbers are
useful for handling ambiguous data and vague linguistic expressions (Bai and Sarkis,
15
2013). A grey based-TODIM approach can take advantage of behavioral and data
variations (Sen et al., 2015).
Most grey MCDM approaches use some heuristics, sometimes unjustified, or
they perform a transformation in the dataset. For example, Sen et al. (2015) utilized
crisp weights for the evaluation criteria in their grey-TODIM. Dou et al. (2014)
applied a grey aggregation method, a variation of the CFCSs (Converting Fuzzy data
into Crisp Scores) defuzzification method, which arrives at crisp values.
Consequently, in order to consider the decision maker’s psychological behavior,
solving an MCDM problem entirely with grey information, without a requirement for
transformation to crisp data, can help make these evaluations more efficient.
TODIM requires relative attribute weights to be determined, limiting its
application. Using lessened decision-maker input, BWM is capable of computing the
attributes’ relative weights; making it easier and more efficient to apply. Fewer
decision-maker interactions and inputs can prove more advantageous for MCDM
techniques due to lack of time, decision-maker fatigue, and lack of interest in
providing information. BWM is extended to incorporate decision-making judgments
under various uncertain and grey environments. Table 3 provides a summary of some
recent papers that apply BWM and the context.
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Table 3: Some recent papers that apply BWM and the context
Method(s) Context Author(s)
BWM Supply chain social sustainability assessment Badri Ahmadi et
al. (2017b)
Fuzzy BWM-COPRAS Analyzing key factors of sustainable
architecture
Mahdiraji et al.
(2018)
BWM-ELECTRE Decision framework for effective offshore
outsourcing adoption Yadav et al .(2018)
BWM A supply chain sustainability innovation
framework and evaluation methodology
Kusi-Sarpong et
al.(2018)
BWM-VIKOR Assessing airline industry service quality Gupta (2018a)
BWM-Fuzzy TOPSIS
Evaluating the performance of manufacturing
organizations using Green Human Resource
Management practices
Gupta (2018b)
SERVQUAL-BWM Assessing the quality of airline baggage
handling systems Rezaei et al.(2018)
Taguchi Loss
Function-BWM-VIKOR Airports evaluation and ranking model
Shojaei et al.
(2018)
BWM Measuring different companies’ R&D
performance
Salimi and Rezaei.
(2018)
No previous studies have employed BWM approach to handle the MCDM
problems using uncertain and grey information. The BWM formulation is also
advanced in this study to determine relative weights information for each attribute.
In summary, a Grey-BWM and Grey-TODIM methodology is applied to social
sustainable supplier selection and evaluation using decision-maker opinions and
behavioral characteristics. These combined tools make the methodology more realistic
and flexible.
17
3. Research Methodology
A case study approach is adopted in this study. The study uses industrial managers
from an Iranian manufacturing company. These managers evaluate and select a
suitable supplier based on supplier social sustainability implementation levels. The
company’s respondent managers were selected based on a combination of purposive
and self-selection sampling approaches. The approach and tools utilized to aid this
evaluation are first detailed in this section. Details of the case company, suppliers, and
respondents are presented in section 4.
3.1. Grey number, BWM and TODIM background
To introduce the proposed social sustainability supplier evaluation and selection
decision method, we first describe the interval grey number, followed by BWM and
TODIM background and notation.
3.1.1. Interval grey numbers
Grey system theory (Deng, 1989), is used to treat vagueness and ambiguity in the
human decision-making process. Scholars have successfully applied interval grey
system theory in economics, medicine, geography, agriculture, industry, and supply
chain management (Bai and Sarkis, 2013). Interval grey numbers can effectively
model decision-maker judgments for social sustainability supplier evaluation and
selection decision-making. Definitions and operations of interval grey numbers
include the following:
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Definition 1: An interval grey number [ , ]x x x is defined as an interval with
known lower x and upper x
bounds, but unknown distribution information. That
is,
[ , ] [ ` | ` ]x x x x x x x x (1)
where x is the minimum possible value, x
is the maximum possible value.
Obviously, if x x then the interval grey number x is reduced to a real crisp
number.
Definition 2: Given two interval grey numbers [ , ]x x x and [ , ]y y y , the
basic mathematical operations of the interval grey number are defined by the
following relationships:
[ , ]x y x y x y (2)
[ , ]x y x y x y (3)
[min( , , , ),x y xy xy xy xy max( , , , )]xy xy xy xy (4)
[min( / , / , / , / ),x y x y x y x y x y max( / , / , / , / )]x y x y x y x y (5)
Definition 3: Given two interval grey numbers [ , ]x x x and [ , ]y y y , the
Euclidean distance measure between two grey numbers is:
2 21( , ) ( )2
d x y x y x y (6)
3.1.2. The best-worst method
BWM (Rezaei, 2015) is a comparison-based MCDM technique for determining
attribute weights. BWM needs less pairwise comparison data and inputs than AHP
tools. The results produced by BWM are typically more consistent and robust (Rezaei
et al., 2016). BWM has been used in several fields, such as transportation, supplier
19
selection, risk identification, and supply chain sustainability innovation (Badri
Ahmadi et al., 2017b; Kusi-Sarpong et al., 2018b). BWM (Rezaei, 2015, 2016)
requires the following general steps:
Step 1. Determine a set of decision attributes{ | 1, , }ic i m .
Step 2. Determine the best attribute (most important) B and the worst attribute
(least important) W.
Step 3. Determine the best attribute over all the other attributes. Based on the
response given, a resulting vector of Best-to-Others (BO) { | 1, , }B BiA a i m is
determined; Bia is the preference of the best attribute B over an attribute i.
Step 4. Determine the preference of all attributes over the worst attribute.
According to the response given, a resulting vector of Others-to-Worst (OW)
{ | 1, , }T
W iWA a i m is determined. iWa is the preference of an attribute i over the
worst attribute W.
Step 5. Compute the optimal weights *{ | 1, , }iw i m . The optimal weights of the
attributes will satisfy the following requirements:
min max{| |,| |}iBBi iW
ii W
wwa a
w w
(7)
subject to:
1 0i i
i
w for w
Although BWM has been employed in various real-world problems (e.g. Badri
Ahmadi et al., 2017b), a more realistic approach would be to use grey numbers due to
decision maker uncertainty and subjectivity. In addition, because TODIM requires
20
relative weights, not weights of attributes, BWM alterations are needed. See section
4.2 expression (12) for the new formulation.
3.1.3. The TODIM method
TODIM (Gomes and Lima, 1992), is a discrete alternative MCDM method based
on prospect theory. TODIM is useful for solving MCDM problems that consider
decision-maker behaviors (Zhang and Xu, 2014). The method consists of two main
stages. In the first stage, the prospect value function is generated to measure the
dominance degree of each alternative over other alternatives. It reflects the
decision-maker’s behavioral characteristic, such as reference dependence and loss
aversion. In the second stage, the overall prospect value of each alternative is
calculated and ranked. TODIM has been applied in various fields of MCDM,
including green supply chain management (Tseng et al., 2014).
In the TODIM method, initially let { | 1, , }js j n represent the n alternatives,
facing the decision-makers, and let { | 1, , }ic i m be the m attributes. Let jix be
the performance score for alternative js with respect to an attributeic . Let
iw
indicate attributeic ’s weight. The TODIM method has the following steps:
Step 1. Normalize the decision matrix [ ]ji n mX x using a normalization
method.
Step 2. Calculate the relative weight wir of attribute ci to the reference attribute cr
using expression (8):
, 1, ,iir
r
ww i r m
w (8)
21
where iw is the weight of the attribute
ic , max{ | 1, , }r iw w i m .
Step 3. Calculate the dominance degree of js over each alternative ks for
attribute ci using expression (9):
1
1
( ) 0
( , )
1( ) 0
irji ki ji kim
ir
i
i j km
ir
iki ji ji ki
ir
wx x if x x
w
s s
w
x x if x xw
(9)
where is the attenuation factor of the losses. 0ji kix x indicates the gain of
alternative js over alternative ks for attribute ci , and 0ji kix x shows the loss
of alternative js from alternative ks for attribute ci.
Step 4. Calculate the overall dominance degree of alternative js over alternative
ks , for all attributes and alternatives using expression (10):
1
( , ) ( , ), ( , )m
j k i j k
i
s s s s i j
(10)
Step 5. Obtain the global value of alternative js using expression (11):
1 1
1 1
( , ) min ( , )
1, ,
max ( , ) min ( , )
n n
j k j kj
k kj n n
j k j kjj
k k
s s s s
j m
s s s s
. (11)
Step 6: Sort the alternatives by their value j .
In order to obtain integrate realistic uncertainties and ambiguities we extend
TODIM to incorporate grey numbers. In TODIM method applications, attributes
relative importance weights are needed; however, no effective method exists for
obtaining these relative weights. This issue limits the TODIM application. To fill this
22
gap, in this paper, we apply grey-BWM for computing the social sustainability
attributes relative importance weights.
4. A Case application
4.1. Case problem description
Iran, the case country of this study is an emerging economy nation in
Southwestern Asia with relatively early stage sustainable development
implementations. The manufacturing sector is especially immature with respect to
social sustainability development (Ghadimi et al., 2017; Mani et al., 2016 a, b).
The decision attributes framework and decision support system introduced in this
paper is utilized in this case manufacturing company setting. The case company is
called “company B” henceforth. Company B (the buying firm) was established in
1966 and after two years in operations initiated production of the Citroen Dyane
model vehicle. Company B has recently formed several joint partnerships with a
number of automobile manufacturing companies in other countries including Korea
and Japan. Different vehicle types are assembled and manufactured by this
corporation. Passenger cars and sport utility vehicles (SUVs) in diverse
manufacturing sites are manufactured. This firm plays a key role in the Iranian
automotive industry. In 2013, company B had a 40 percent market share and became a
dominant player in the Iranian passenger vehicle sales market
(www.businessmonitor.com/autos/iran).
Company B has planned to improve its social sustainability performance due to a
series of concerns and pressures from various local activists (Zailani et al., 2015).
23
Since most automobile parts are outsourced to suppliers; selecting the appropriate
suppliers based on social sustainability performance can help improve the buying
company’s social performance. Supplier selection is an important starting point to
redeem company B’s social image. Building corporate competitive advantage can also
occur with appropriate supplier selection. They have taken a strategic stance by
focusing on social sustainability supply chain performance. This strategic stance is
supported by selecting a socially conscious parts suppliers. Supplier social
sustainability implementation levels are used to evaluate the suppliers.
We selected the Iranian automobile manufacturing company (the case company)
based on its long existence and operations, which span over 5 decades. Additionally,
it has the largest vehicle market share in Iran. Management was interested in this topic
as part of its strategic mission. We then purposefully selected experienced and
knowledgeable managers who are familiar with the various issues of this study. We
identified 14 potential managers and invited them, allowing for self-selection for
those who wished to be involved in the study. This self-selection provided us with
managers who were willing to commit to the study. This process resulted in 10 of the
managers accepting to participate with 4 managers declining.
We then formed a ten member decision making team including a supply manager,
assistant supply chain manager, purchasing manager, finance manager, research and
development manager, IT manager, production manager, general manager, logistics
manager and maintenance manager. We proceeded with this number of managers
because we consider it sufficient for providing reliable results; especially from an
24
individual case company. Also when compared to a number of studies in the published
literature, there are many that have used 5 or fewer experts (e.g. Dou et al., 2014; Gupta
and Barua, 2018). In addition and most recently, Rezaei et al. (2018) in their paper on
evaluating quality of baggage handling at airports, made it clear that only 4-10 experts
are required for getting reliable data for MCDM analysis. Another recently published
paper in IJPR on supply chain sustainability innovation used only 5 experts in their
BWM analysis.
Each manager had more than 10 years working experience and was specifically
formed to partake in the evaluation process. Table 4 presents the characteristics of
managers who were involved in the decision-making process from the case company.
Table 4: Respondent managers from the case company involved in the
decision-making process
Expert Position Role
Working
Experience
(Years)
1 Supply Manager Management of sourcing
contract and warehouse 10
2 Assistant Supply Chain Manager Management of sourcing
contract and warehouse 11
3 Purchasing Manager
Management of
purchasing program
implementation and
training
15
4 Maintenance Manager Management of
maintenance activities 18
5 Finance Manager Management of company's
financial budgetary 17
6 Research and Development (R&D)Manager Management of R&D
related activities 20
7 IT Manager Management of
Information Technology 22
25
Management then shortlisted five suppliers from their supply-base. These five are
Company B’s top suppliers and are evaluated in this study. Characteristics of these
suppliers are provided in Table 5.
Table 5: Suppliers characteristics
Supplier Location Year of
establishment
Workforce
size Turnover ($)/year
Supplier 1 Tehran 1999 465 25,000,000
Supplier 2 Tehran 2005 352 20,000,000
Supplier 3 Tehran 1983 143 30,000,000
Supplier 4 Tehran 2009 365 21,000,000
Supplier 5 Tehran 1980 215 22,000,000
4.2. Applying Grey-BWM and Grey-TODIM to Sustainable Supplier Selection
The Grey-BWM and Grey-TODIM methodology is now applied to the case. The
proposed social sustainability supplier evaluation and selection model consists of nine
steps. The methodology identifies the ranking of suppliers based on their social
sustainability performance.
program implementation
8 Production Manager Management of different
areas of production 10
9 General Manager
Management of the firm's
marketing and sales
functions as well as the
daily business operations
13
10 Logistics Manager
Management and
implementation of
complex operations in
order to meet customers’
needs
19
26
Step 1: Construct the social sustainability decision system.
The decision system for investment evaluation and selection of the socially
sustainable supplier is initially defined. The system is defined by T = (S, C), where S
= {s1, s2, ..., sm} is a set of m socially sustainable suppliers, and C = {c1, c2, ... , cn} is a
set of n social sustainability attributes. For this empirical case, let S = {sj, j = 1,
2,...,5} and C = {ci, i = 1, 2,...,8}.
This study uses eight social sustainability attributes using a framework from the
literature (Badri Ahmadi et al., 2017b). The framework includes: work safety and
labor health (SSA1), training education and community influence (SSA2), contractual
stakeholders’ influence (SSA3), occupational health and safety management system
(SSA4), the interests and rights of employees (SSA5), the rights of stakeholders
(SSA6), information disclosure (SSA7), and employment practices (SSA8), see Table
1.
The ten supply chain managerial decision makers, see the previous section, are
denoted by E ={ | 1, ,10}eE e . They have been involved to some level with
sustainable supplier management.
Step 2: Determine the best and the worst attribute.
In this step, each expert (eE ) was asked to determine the best and the worst
attribute (i), among all 8 social sustainability attributes. As an example, the best and
worst attributes identified by each of the ten experts are displayed in Table 6.
27
Table 6: The best and worst attributes determined by experts 1-10
Experts Most important attribute Least important attribute
Expert1 EP (SSA8) IRE (SSA5)
Expert2 RS (SSA6) ID (SSA7)
Expert3 WSLH (SSA1) CSI (SSA3)
Expert4 ID (SSA7) EP (SSA8)
Expert5 WSLH (SSA1) TECI (SSA2)
Expert6 CSI (SSA3) EP (SSA8)
Expert7 WSLH (SSA1) ID (SSA7)
Expert8 OHSMS (SSA4) TECI (SSA2)
Expert9 IRE (SSA5) TECI (SSA2)
Expert10 CSI (SSA3) TECI (SSA2)
Step 3: Determine the best attribute preference over all attributes and all attributes
preference over the worst attribute.
In the third step, each expert (eE ) was asked to specify the best attribute’s
preference over all other attributes, using a linguistic measurement ranging from
‘Equal importance’ (EqI) to ‘Extreme importance’ (ExH), which results in a vector of
Best-to-Others (BO) { | 1, ,8}e e
B BiA a i . Next, each expert (eE ) was also asked to
determine the preference of all attributes over the worst attribute, again using a
linguistic measurement ranging from ‘Equal importance’ (EqI) to ‘Extreme
importance’ (ExH), which results in the vector of Others-to-Worst
(OW) { | 1, ,8}e e T
W iWA a i .
In our case, this step results in ten BO evaluation matrices and ten OW evaluation
matrices for all experts. As an example, the BO evaluation and OW evaluation
matrices for expert (1E ) is presented in Table 7 and Table 8. For brevity, the
remaining 18 matrices are not shown.
28
Table 7: The linguistic responses and grey number of the Best-to-Others evaluation
matrix for Expert 1.
Type The best
attribute WSLH TECI CSI OHSMS IRE RS ID EP
Linguistic EP
LI MI LI WI SI MpI MI EqI
Grey [2.5,3.5] [3.5,4.5] [2.5,3.5] [1,2.5] [5.5,6.5] [4.5,5.5] [3.5,4.5] [1,1]
Table 8: The linguistic responses and grey number of the Others-to-Worst evaluation
matrix for Expert 1.
Type Linguistic Grey
The worst
attribute IRE
WSLH LI [2.5,3.5]
TECI WI [1,2.5]
CSI LI [2.5,3.5]
OHSMS MI [3.5,4.5]
IRE EqI [1,1]
RS LI [2.5,3.5]
ID MI [3.5,4.5]
EP SI [5.5,6.5]
Step 4：Transform linguistic responses into interval grey numbers.
To deal with human judgment obscurity and ambiguity, the linguistic responses
are transformed into interval grey numbers. An interval grey numerical scale table and
its corresponding linguistic measurements are shown in Table 9.
As an example, the preference value shows little importance (LI) of the EP
(SSA8) attribute over the WSLH (SSA1) attribute and is transformed into a grey
number for expert 1E to be:
1
1Ba = LI = [2.5,3.5]. A grey BO matrix e
BA and grey
29
OW matrix e
WA from the linguistic matrix is identified in this step, which can be seen
in the third row of Table 7 and the third column of Table 8.
Table 9: Linguistic/Human judgments and their corresponding interval grey numbers.
Linguistic/Human judgments Interval grey numbers
Equal importance (EqI) [1,1]
Weak importance (WI) [1,2.5]
Little importance(LI) [2.5,3.5]
Moderate importance (MI) [3.5,4.5]
Moderate plus importance (MpI) [4.5,5.5]
Strong importance (SI) [5.5,6.5]
Strong plus importance (SpI) [6.5,7.5]
Very strong importance (VsI) [7.5,8.5]
Extreme importance (ExI) [8.5,10]
Step 5： Calculate the relative weights *
riw for social sustainability attributes.
TODIM requires relative weight values. To do so, BWM needs adjustment to
calculate relative weights rather than absolute weights. The social sustainability
attributes relative weights are calculated by solving the Grey-BWM optimization
model for each expert eE using expression (12).
min max{| |,| |}ee
e erirBBi iWe ei
ri rW
wwa a
w w
(12)
s.t.
0 1e e
ri riw w
max 1e
rii
w
30
The relative weights of each social sustainability attribute (ci ), from each
expert (eE ) are computed to obtain a relative weight vector. The value in the first
ten columns of Table 10 is the relative weight value for each expert opinion. As
can be seen in Table 10, the consistency ratio ( * ) is small according to the
consistency index table of Rezaei (2015), hence the comparisons are highly
consistent and reliable.
Table 10: The social sustainability relative attribute weights for the 10 experts using
BWM
Attributes Expert1 Expert2 Expert3 Expert4 Expert5 Expert6 Expert7 Expert8 Expert9 Expert10 Average
WSLH [0.48,0.66] [0.34,0.41] [1,1] [0.47,0.77] [1,1] [0.23,0.74] [1,1] [0.59,0.67] [0.46,0.63] [0.43,0.7] [0.6,0.76]
TECI [0.24,0.54] [0.33,0.54] [0.45,0.69] [0.39,0.6] [0.11,0.12] [0.46,0.94] [0.65,0.86] [0.12,0.13] [0.12,0.19] [0.11,0.11] [0.3,0.47]
CSI [0.48,0.66] [0.45,0.65] [0.12,0.15] [0.59,0.67] [0.19,0.57] [0.94,1] [0.26,0.55] [0.38,0.78] [0.42,0.44] [0.96,1] [0.48,0.65]
OHSMS [0.67,0.75] [0.49,0.6] [0.39,0.94] [0.22,0.75] [0.47,0.68] [0.56,0.6] [0.51,0.71] [1,1] [0.66,0.91] [0.38,0.59] [0.54,0.75]
IRE [0.13,0.14] [0.27,0.48] [0.26,0.52] [0.27,0.55] [0.22,0.52] [0.17,0.43] [0.2,0.36] [0.39,0.55] [1,1] [0.47,0.49] [0.34,0.5]
RS [0.2,0.34] [0.94,1] [0.45,0.69] [0.33,0.41] [0.33,0.88] [0.27,0.54] [0.24,0.58] [0.19,0.62] [0.21,0.33] [0.63,0.66] [0.38,0.61]
ID [0.27,0.49] [0.13,0.14] [0.26,0.55] [0.76,1] [0.17,0.36] [0.29,0.54] [0.14,0.15] [0.23,0.55] [0.42,0.44] [0.23,0.47] [0.29,0.47]
EP [0.96,1] [0.43,0.78] [0.2,0.35] [0.11,0.13] [0.58,0.8] [0.15,0.16] [0.26,0.55] [0.19,0.62] [0.26,0.5] [0.37,0.42] [0.35,0.53]
* 1.63 1.72 1.60 2.50 2.72 1.86 1.77 2.88 1.61 2.40 2.07
We then determine an average relative weight *
riw for all the experts eE using
expression (13).
* 1 21[ ]E
ri ri ri riw w w wE
(13)
In our case, as an example, the average relative weight for attribute WSLH
( *
1rw ) is: 10 10
*
11 1
1 1
1[( ), ( )] [0.60,0.76]
10
e e
rr r
e e
w w w
.
31
The average relative weight grey number values are shown in the last column of
Table 10.
Step 6: Evaluate the supplier performance for each social sustainability attribute.
In this step, each expert (eE ) is asked to evaluate each supplier (sj) with respect to
the eight social sustainability attributes (ci). The evaluations for social sustainability
attributes are verbal descriptions ranging from 'Very Good (VG)' to 'Very Poor (VP)'.
An interval grey numerical scale with its corresponding performance verbal values is
given as: Very Good [8, 10], Good [6, 8], Medium [4, 6], Poor [2,
4], Very Poor [0, 2]. This step will result in ten grey matricese
jix . As an
example, the evaluation grey matrix of an expert (1E ) is presented in Table 11. For
brevity, the remaining nine matrices are not shown.
Table 11: The grey number for social sustainability attributes of suppliers for Expert
1.
Suppliers WSLH TECI CSI OHSMS IRE RS ID EP
supplier 1 [4,6] [0,2] [4,6] [2,4] [8,10] [8,10] [0,2] [2,4]
supplier 2 [6,8] [8,10] [6,8] [4,6] [8,10] [6,8] [4,6] [2,4]
supplier 3 [0,2] [6,8] [4,6] [8,10] [2,4] [8,10] [4,6] [2,4]
supplier 4 [4,6] [2,4] [6,8] [0,2] [2,4] [8,10] [2,4] [0,2]
supplier 5 [2,4] [4,6] [0,2] [2,4] [8,10] [6,8] [4,6] [2,4]
In our case, expert 1E thinks that supplier s1 is a “Medium” level on the WSLH,
(SSA1) attribute and then assigns a linguistic value of M (i.e.1
1,1
e Mx ); identified
as: 1
1,1
ex = M = [4, 6].
Step 7: Aggregated performance levels of suppliers for each social sustainability
attribute.
32
We seek to arrive at an aggregated performance grey matrix of suppliers for
all social sustainability attributes and all experts using expression (14).
1 21[ ] ,E
ji ji ji jix x x x i jE
(14)
As an example calculation, the grey value for supplier s1, attribute c1 ( 11x ) is:
11
1[(4 ), (6 )] [3.56,5.45]
10x . The overall aggregate grey attribute
values results for each supplier are presented in Table 12.
Table 12: The aggregate grey values (ijx ) of each suppliers for all experts.
Suppliers WSLH TECI CSI OHSMS IRE RS ID EP
supplier 1 [3.56,5.45] [4.18,5.74] [5.32,7.1] [3.98,5.87] [4.04,5.71] [4.7,6.48] [3.74,5.41] [4.2,5.87]
supplier 2 [3.58,5.36] [2.5,4.39] [3.8,5.69] [4,5.78] [4.48,6.26] [4.9,6.79] [4,5.78] [5.52,7.3]
supplier 3 [1.98,3.87] [3.8,5.69] [3.56,5.56] [4.92,6.48] [3.1,4.88] [4.92,6.7] [4,6] [3.98,5.87]
supplier 4 [3.12,5.01] [3.54,5.54] [3.36,5.14] [4.62,6.4] [4.42,6.2] [3.6,5.27] [2.88,4.66] [3.3,5.19]
supplier 5 [2.44,4.33] [2.9,4.79] [2.64,4.53] [4.2,6.09] [3.82,5.71] [5.34,7.01] [4,5.78] [4.2,5.65]
Step 8: Determine the overall dominance measures of each supplier.
The target of this step is to identify the overall dominance measures of the suppliers.
The attenuation factor ( ), see expression (9), of the losses is set to =12 which has
the range of values 0< <
*
1
*
m
ri
i
ri
w
w
.
First, the dominance measure for each social sustainability attribute (ci) is
determined by expression (15).
33
1 1
1
1
1 1
[ ( ), ( )] 0
01( , ) [ ( ), ( ) ]
0
1 1[ ( ), ( )] 0
ri riji ki jiki ji kim m
ri ri
i i
m
riji kii ri
ji kii j k ki jim
ri kijiri
i
m m
ri ri
i iji ki kiki ji ji
ri ri
wwx x x x if x x
w w
w if x xws s x x x x
w and x xw
w w
x x x x if x xw w
(15)
As an example, the following computational processes of the dominance
measures are presented using expression (13), where = 12. The interval grey value
of supplier s1 is [3.56, 5.45] and of supplier s2 is [3.58, 5.36] for the WSLH (SSA1)
attribute. Then we can obtain 1,1 1,2x x = -1.87 < 0 (a loss) and 11
1,21,1
eex x = 1.8
(again),
*
*
1 11,1 1,21 2 1,2 1,1*
*1
1
1( , ) [ ( ), ( )]
12
m
ri
i
m
ri
WSLH
i
ww
s s x x x xw
w
= [-0.32, 0.54].
The second sub-step uses expression (10) to determine the overall dominance
measures for each supplier.
For example, the dominance measure for all social sustainability attributes
between suppliers 1s
and 2s are 1 2 1 2
1
( , ) ( , )m
i
i
s s s s
= [-0.32, 0.54] +
1 2
2
( , )m
i
i
s s
= [-3.03, 3.48]. The overall dominance measures for social sustainability
attributes between suppliers are shown in Table 13.
34
Table 13: The overall dominance measures for social sustainability attributes between
suppliers.
Suppliers supplier 1 supplier 2 supplier 3 supplier 4 supplier 5
supplier 1 [-3.05,3.72] [-3.03,3.48] [-3.33,3.13] [-3.51,2.76] [-3.38,2.79]
supplier 2 [-2.87,3.71] [-3.14,3.8] [-3.26,3.23] [-3.46,2.83] [-3.4,3.14]
supplier 3 [-2.66,4.08] [-2.71,3.98] [-3.16,3.82] [-3.23,3.47] [-3.19,3.62]
supplier 4 [-2.23,4.25] [-2.25,4.17] [-2.81,3.84] [-3.14,3.8] [-2.68,3.75]
supplier 5 [-2.24,4.16] [-2.62,4.16] [-2.99,3.87] [-3.15,3.36] [-3.11,3.77]
Step 9: Determine the global value for each supplier.
In this step, the global value j of the supplier js for all social sustainability
attributes is determined using expression (16).
1 1
1 1
( ( , ),min ( , ))
1, ,
(max ( , ),min ( , ))
n n
j k j kj
k kj n n
j k j kjj k k
d s s s s
j m
d s s s s
. (16)
In our case, the sum of the overall dominance measures of the supplier 1s
for the
social sustainability attributes are 1
1
( , )m
k
k
s s
[-13.05, 19.92]. The minimum values
of the overall dominance measures sums over all suppliers for social sustainability
attributes are 1
min ( , )m
j kj
k
s s
[-16.50, 16.22]. The maximum values of the overall
dominance measures sums over all suppliers for social sustainability attributes and
expert 1E are
1
max ( , )m
j kj
k
s s
[-13.05, 19.92]. Thus, the global value 1 of
1s
overall social sustainability attributes is
1
1 11
1 1
( ( , ),min ( , ))
(max ( , ),min ( , ))
n n
k j kj
k k
n n
j k j kjj k k
d s s s s
d s s s s
=
35
0.843. The global values and rankings of supplier’s social sustainability are given in
Table 14.
Table 14: The global values and rankings of suppliers.
Suppliers j Ranking
supplier 1 0.843 2
supplier 2 1.000 1
supplier 3 0.362 3
supplier 4 0.000 5
supplier 5 0.183 4
The global measures and the ranking order of all suppliers can be found in
Table 14. Using Table 14 information, we can conclude that supplier2s , has the
highest social sustainability performance according to managerial opinion with a
score of 1.000.
5. Sensitivity Analysis
In this section, the values of the basic TODIM attenuation parameter are
altered to investigate the results’ robustness. A sensitivity analysis is also conducted
for each expert.
5.1. Sensitivity analysis for the attenuation factor
In the initial results, the losses attenuation factor was set to 12. The different
choices of lead to different shapes of the prospect theoretical value function in the
negative quadrant. The attenuation factor means how much the losses will
contribute to the global value.
36
We now complete a sensitivity analysis to determine the robustness of the
solution. Because
*
1
*16.27
m
ri
i
ri
w
w
, we select ranges of 1 ≤ ≤ 16, in increments
of 1. Figure 1 summarizes results of this sensitivity analysis.
Figure 1. Final global value of suppliers for different values
As can be seen in Figure 1, the supplier 2s is the best supplier for the range of θ
values. This result shows that the ranking of suppliers is relatively robust and the
managers can be confident of the supplier social sustainability ranking.
5.2. Sensitivity analysis for each expert
Another sensitivity analysis is completed to determine the impact of decision
maker/manager (we use the term expert from now on for simplicity) beliefs on the
final results. We will compute the global value of each supplier for each responding
expert eE , with the same processes as demonstrated (initially) in the case within
37
section 4.2. The results of this sensitivity analysis can be found in Table 15 and
Figure 2.
Table 15: The global value of social sustainability attributes and each expert for
suppliers.
Suppliers Expert1 Expert2 Expert3 Expert4 Expert5 Expert6 Expert7 Expert8 Expert9 Expert10 Average
supplier 1 0.277 0.309 0.629 0.951 0.750 0.861 0.201 0.925 0.407 0.765 0.607
supplier 2 1.000 1.000 0.363 0.979 0.738 0.263 0.621 0.311 0.786 0.503 0.656
supplier 3 0.551 0.630 0.245 0.158 0.000 0.126 1.000 0.000 0.970 1.000 0.468
supplier 4 0.000 0.000 0.000 0.566 0.923 0.000 0.000 0.580 0.728 0.768 0.356
supplier 5 0.217 0.070 1.000 0.016 0.952 1.000 0.162 0.964 0.000 0.000 0.438
Figure 2: The global value for each supplier and each expert.
The results for the highest ranked supplier do change across each individual
expert’s evaluation. Figure 2 shows that all supplier rankings demonstrate
inconsistencies and fluctuations according to the ten expert opinions.
Supplier 2, the most preferred socially sustainable supplier for the aggregate case,
showed some stability across expert evaluations. Supplier 2 is highest ranked for
38
experts 1, 2 and 4, but is lowest weighted by expert 6 and ranked as the third most
important supplier; although it is ranked in fourth place by expert 10.
Supplier 4 is the worst socially sustainable supplier in the initial case and also
showed relative stability across expert evaluations. Supplier 4 has the worst ranking
based on the opinions of experts 1, 2, 3, 6 and 7. Moreover, the best global value of
Supplier 4, belongs to the second ranked supplier according to expert 5.
Supplier 5, ranked as the fourth overall as a socially sustainable supplier, showed
the biggest conflicting results across individual expert evaluations. Based on Figure 2,
Supplier 5 is identified as the worst ranked supplier three times by experts 4, 9 and 10.
Supplier 5 is the best socially sustainable supplier four times, based on the opinions of
experts 3, 5, 6 and 8. This volatility and spread will require critical investigation and
discussion amongst the experts to more fully comprehend the variations.
Although Supplier 1 was not determined as the best supplier in the overall expert
evaluations, it was identified as the second ranked socially sustainable supplier. We
may conclude that supplier 1 has a comparatively stable ranking across all expert
evaluations.
Supplier 3 also showed some of the most significant conflicting results across
expert evaluations. Based on Figure 2, Supplier 3 was twice identified as the worst
performer based on the opinions of experts 5 and 8; while being determined as the
best socially sustainable supplier according to the opinions of experts 7 and 10.
39
Practically, these results show the difficulties with maintaining consistency across
expert evaluations. It provides insights into possible misapplication issues of the
Grey-BWM and Grey-TODIM methodology. The results practically show that
including only particular decision-makers into the decision cycle may provide
misleading or biased selection results. Thus, care needs to be taken in the
determination of decision-makers for the application of this methodology and that a
discussion and consensus needs to be formed after some initial evaluations.
The average global values are shown in the last column of Tables 15, and are
consistent with the results of the initial case. However, the average global values are
more valuable than the global values of the initial case for decision-makers and
supply chain managers. Average global values, which are normalized, can more
effectively evaluate relative dominance degree or gap between two suppliers.
6. Discussion and Implications
The empirical results of the case illustration of this methodology are summarized
in Table 14. These results depict the global values for five potential suppliers, along
with their respective rankings. Supplier 2 was ranked the top supplier with a global
value of 1. Suppliers 1, 3, 5, and 4 follow, respectively. Even though supplier 2 is
considered the best supplier from this result, and is recommended for contracting by
the Iranian manufacturing company, there are some social sustainability criteria that
had low ratings for supplier 2. For implementation of this selection recommendation,
the Iranian manufacturing company may require specific post-selection negotiations
40
with this supplier for possible improvements in these lower rated performance
criteria; using the other suppliers as benchmarks.
We now illustrate from the case how managers can use such results as a guide in
negotiating with the selected supplier for future performance improvements and
supplier development. As a benchmark example, using data from Table 12, Supplier 1
has the highest rated performance criteria amongst the five suppliers for the first three
social sustainability criteria, namely: “work health and safety” (WSLH/SSA1),
“training education and community influence” (TECI/SSA2) and “contractual
stakeholders’ influence” (CSI/SSA3). For these three criteria, supplier 1’s
performance ratings can be considered as a benchmark measurement for other
suppliers. Therefore, the Iranian manufacturing company can, as part of their
post-supplier selection project, consider negotiating with supplier 2 to focus on
improving these three performance criteria (WSLH/SSA1, TECI/SSA2 and
CSI/SSA3). Given the possibilities of interactions and tradeoffs, care must be taken
not to compromise the overall performance of supplier 2. Thus, a supplier
development process may be put into place that may help improve supplier 2 in a
balanced way.
It is also observed from Table 12 that supplier 3 has the best rated performance
for “occupational health and safety management system” (OHSMS/SSA4) and
“information disclosure” (ID/SSA7). Using these two highest rated performance
criteria of supplier 3 as a benchmark, the Iranian manufacturing company may use
41
this benchmark in their post-selection negotiation with supplier 2 (the optimal
supplier), to request improvement in these criteria (OHSMS/SSA4 and ID/SSA7)
overtime. Further scanning through Table 12 information depicts that supplier 5 has
the highest rated performance for “the right of stakeholders” (RS/SSA6) criteria. The
Iranian manufacturing company may, during the post-selection negotiating phase,
request supplier 2 to improve overtime its performance on (RS/SSA6). Supplier 2 has
the best rated performance for “the interests and rights of employees” (IRE/SSA5) and
“employment practices” (EP/SSA8) criteria.
These results and perspectives show that compensatory evaluations may allow
some poorly performing results to occur; setting minimum value expectations may be
necessary to guarantee better overall performance on factors. A practical concern is
that trying to achieve best in class for each metric may not be possible or quite capital
intensive. Buyers should take care in making these requested changes without some
supportive collaboration and coordination with the selected supplier.
7. Summary and Conclusion
According to RBV, companies can gain competitive advantage by developing
resources that help to differentiate themselves from other competitors because it is
valuable and difficult to replicate. Social sustainability can be an important intangible
resource. Organizational social sustainability can be enhanced by having a socially
sustainable supply chain. To help build a socially sustainable effective supply chain
42
supplier evaluation and evaluation is required. This supplier evaluation and selection
is where MCDM tools are helpful.
Although a variety of tools have been developed and applied for this purpose,
each have their limitations and are context dependent in their effectiveness. In this
study, to address a few contextual limitations of other techniques and applications, we
utilized an integrated MCDM tool composed of grey numbers, BWM and TODIM to
investigate social sustainability supplier evaluation and selection.
This work introduced a comprehensive framework for investigating and
supporting social sustainability supplier evaluation and selection. The framework
consists of eight social sustainability attributes including: ‘Work health and safety’
(WSLH/SSA1); ‘Training education and community influence’ (TECI/SSA2);
‘Contractual stakeholders’ influence’ (CSI/SSA3); ‘Occupational health and safety
management system’ (OHSMS/SSA4); ‘The interests and rights of employees’
(IRE/SSA5); ‘The rights of stakeholders’ (RS/SSA6); ‘Information disclosure’
(ID/SSA7); and ‘Employment practices’ (EP/SSA8). The social sustainability
framework was then applied in an Iranian manufacturing company with inputs from
ten of their industrial experts (managers) using the introduced decision support tool
for assessing and ranking five suppliers.
7.1 The novelty and strengths of the methodology
43
There are a number of novel contributions which provide advantages of this
methodology over most existing methodologies for sustainable supplier evaluation
and selection.
First, our proposed method, based on prospect theory (TODIM) and grey system
theory (grey number), takes into account decision maker gain or loss psychological
behavior within uncertain environments. It can yield more credible results; results that
are more in line with decision maker actual opinions. Most methods of sustainable
supplier selection fail to simultaneously consider decision maker psychological
behavior and sustainability decision uncertainty. The proposed method also allows
multiple decision makers to evaluate social sustainable suppliers using their
experience and knowledge.
Second, BWM is used to identify the relative weights of attributes and addresses
the gap of TODIM requiring this additional information. The relative attribute weights
information from BWM are more reasonable and represented by grey numbers.
AHP/ANP may also be used to determine the relative attribute weights. BWM is
advantageous since it requires less pairwise comparison information and decision
maker inputs ( 2 n ) rather than AHP tools ( n n ) given n attributes.
Third, traditional BWM is used to determine the absolute weights of attributes. It
needs additional steps to convert these absolute weights to relative weights; increasing
computational complexity. We extended grey-BWM to optimize and determine the
relative weights of attributes by modifying the objective function and introducing
grey numbers.
44
This hybrid group decision method can be applied to quantitatively express the
psychological behavior of the decision makers in a group decision and in an uncertain
environment. Thus, it can strengthen group decision making process
comprehensiveness, and can be successfully applied to various sustainability decision
making problems.
7.2. Limitations and future research directions
Every study has limitations and this study is no exception. However, these
limitations can serve as a basis for future studies. One of the key limitations is that the
results are based on a single evaluation tool (grey-based BWM-TODIM), therefore,
the findings are sensitive to the assumptions of these models for the case company’s
social sustainability supplier selection. More tools and factors (e.g. economic,
environmental) can be applied in this case and the results compared, and a final
decision made. Another limitation of this study is that, the criteria weights and
ranking of the suppliers were determined using grey-BWM and grey-TODIM
respectively. We suggest that possible future researches apply other MCDM models
to determine the weight of the social sustainability criteria and use a number of other
MCDM models including TOPSIS or ANP to evaluate and rank the suppliers.
Acknowledgements: This work is supported by the National Natural Science
Foundation of China Project (71472031, 71772032).
45
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